Industries
AI for telecom that holds at carrier scale.
Telecom runs at a volume no support team can staff and a churn rate no quarter forgives, under a regulator watching every message and call. We build production AI for operators: support that deflects the repeatable contacts, network operations that see incidents sooner, and retention that acts before the customer leaves, with consent, DND and TRAI handled in the design, not bolted on. Working worldwide since 2019.
Who builds production AI for telecom operators?
extendfuture, an AI agency working worldwide since 2019. For telecom we build support deflection that resolves the repeatable contacts, network-operations copilots that triage alerts and shorten incidents, churn and retention models that act before a customer leaves, and back-office automation across provisioning, billing and order management. We have not shipped under a telecom logo yet; what we run in production is every primitive a telecom program is built from: voice agents, transcription at scale, conversational support personas and predictive models. Every system ships with least-privilege access, audit logs, consent and DND enforcement and human approval gates, designed to support TRAI regulations and DPDP expectations. Engagements start with a founder-led call.
The problems telecom teams bring us
The pattern is consistent across mobile, broadband and enterprise connectivity. Contact volume scales with the subscriber base, but headcount cannot, and most of it is the same handful of questions. The network throws more alerts than any operations center can read, so real incidents hide in the noise. Churn is visible only after the customer has gone. And every outbound message sits under a regulator that fines first. Generic AI tools ignore that last constraint, which is usually when we get the call.
- Tier-1 contact volume that grows with the subscriber base and repeats the same questions
- Network alerts in the thousands, real incidents buried in duplicates and noise
- Churn spotted only in the next month's numbers, too late to act
- Outbound campaigns exposed to TRAI, DND and consent rules that fine first
- Billing disputes and provisioning errors that eat back-office hours
- A pilot that demoed well and stalled before it touched production traffic
What we build for telecom
The recurring shape is high volume, tight rules and expensive exceptions. That is a digital-worker and agentic-AI problem with a human layer, and it is what we build. Each service has a scoped entry engagement, so the first commitment is small.
- Support deflection across chat, voice and WhatsApp: digital workers that resolve the repeatable contacts and route the rest to a person with context
- Network-operations copilots that cluster alerts, gather context and draft incident summaries, via agentic AI development, with humans on every action
- Churn and retention models that flag at-risk accounts with the reason attached and feed a retention workflow
- Back-office automation for provisioning, billing disputes and order management, via AI process automation, one workflow at a time
- Call and chat analytics: transcription at scale turned into QA, intent and compliance signals
- Human-in-the-loop review where a wrong outbound contact is a regulatory incident, not a bad email
Proof: the primitives, in production
We are honest that telecom is a priority vertical we are building into, not a logo we already have. What lets us start fast is that every part a telecom program needs, we already run in production elsewhere. The engineering carries over; only the domain changes.
- Voice agents: a voice assistant that turns speech into action at play speed, and a voice AI interviewer inside a hiring platform, the same technology support and network voice need
- Transcription at scale: high-throughput pipelines that turn audio into searchable, structured text, the backbone of call analytics and QA
- Conversational support: a persona customers actually talk to, advisory-first with human escalation
- Predictive models: sales analytics that lifted qualified-lead conversion by roughly a quarter, the same discipline pointed at churn
If you build the software too
Telecom software is a web of services at scale: OSS and BSS, provisioning, billing, order management, the customer app. When AI writes that code, one schema change can break billing for a region or drop a provisioning path, and it surfaces in production where it costs the most. everylayer, our evidence gate, scores what your tests actually prove across seven layers, writes the missing tests as draft pull requests, and gates every change on the right tests at the right layer before it merges.
- Contract tests across provisioning, billing and CRM services, so a breaking change is caught pre-merge, not in shared staging
- Impact-scoped test runs that keep a large service estate shippable at speed
- Self-hosted deployment where subscriber data and source stay inside your network
- The same discipline as our digital workers, applied to your code: prove it before you trust it (/everylayer/)
How an engagement starts
It starts with a founder-led 30-minute call, booked from the contact page. Bring the workflow that hurts: the contact volume you cannot staff, the alert noise, the churn you see too late. We give an honest read on whether AI fits, what it has to beat, and which entry engagement proves it fastest. No discovery phase billed by the month.
- A 30-minute call with a founder, not a sales rep: bring the workflow, the volumes and the rules
- A fixed entry engagement: an Agent Readiness Audit, One Workflow Automated, or an AI Opportunity Map
- A working system on your real contacts or data, with accuracy and deflection reported as numbers, before any commitment to scale
- An honest no-go if the data, the volumes or the economics do not support a build yet
What a telecom AI project needs
Less than most teams expect, but the list is non-negotiable. Support, network and retention projects move fast when these exist on day one.
- Access to the corpus: contact logs, transcripts, billing and usage history, with personal data masked before models see it
- Your consent and DND registry, and the rules that govern outbound contact
- A named owner who can make product calls in days, not a committee
- A quality bar as numbers: the deflection rate, CSAT floor and cost per contact the system must hit
- A clear line on what the AI does alone and what routes to a person, especially for outbound and network actions
- A security posture agreed up front: least-privilege access, audit logs, data residency, designed to support TRAI and DPDP expectations
Do you have a telecom deployment in production?
Not under a telecom logo, and we will not pretend otherwise. What we do run in production is every building block a telecom AI program needs: voice agents that handle real conversations, high-throughput transcription pipelines, conversational support personas customers actually talk to, and predictive models built on operational history. Telecom is a priority vertical for us because those primitives map onto it almost one to one. You get proven engineering and an honest read on what the first telecom build has to prove.
How do you handle TRAI, DND and consent?
As a design constraint, not a disclaimer. Consent and DND (do-not-disturb) status is checked before any outbound message or call, preference and scrubbing rules are enforced in the sending path, and every contact is logged with the consent basis it relied on. The same posture is built to support TRAI regulations on commercial communication and India's DPDP Act. The system will not place a contact it cannot justify to an auditor.
Can AI deflect tier-1 support without hurting CSAT?
That is the design goal. We deflect the repeatable, well-understood contacts, balance, plan, activation, known outages, where a fast correct answer beats a queue, and route anything ambiguous or emotional to a person with full context. Deflection is measured against CSAT and resolution quality, not just call volume, so a drop in satisfaction shows up as a number and gates the rollout. Deflection that annoys customers is a false economy, and we treat it as a failure.
Can you help with network operations and alert fatigue?
Yes, on the triage and assist side. Network operations centers drown in alerts, most of them noise or duplicates of one root cause. We build agents that cluster related alerts, pull the context an engineer would gather by hand, and draft an incident summary, so a human decides and acts faster. The system assists the NOC; it does not take irreversible network actions on its own, and every recommendation is logged.
How do you reduce churn?
By predicting it early enough to act and making the action easy. We build models on your usage, billing and interaction history that flag accounts at risk with the reason attached, then feed that into a retention workflow: the right offer or fix, surfaced to the team or the customer at the right moment. It is the same predictive discipline behind our production sales-analytics work, pointed at retention instead of acquisition.
Our telecom AI pilot stalled. Can you take it over?
Yes, that is a common starting point. Most pilots die between demo and production because the boring half was never built: evals, guardrails, consent enforcement, escalation paths, audit logs and cost ceilings. We audit what exists, add that layer, and either productionize the pilot or tell you plainly why it will not work.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.